Daily Analysis of COVID-19 Data from Johns Hopkins University

           Updated every 6 hours. Last update: 2020-03-31 16:07 UTC

Josef Kellndorfer, Ph.D., Earth Big Data, LLC; Richard Signell, Ph.D., USGS

NOTE: You can hover over plots to see actual numbers.

News 2020-03-31

  • Added charts for top 12 countries confirmed cases and deaths

Content

Introduction: Population Normalization and Logarithmic Scaling
Interacting with the plots

Examples Linear and Logarithmic Scale
Confirmed Cases: USA
Confirmed Cases: Germany
Latest Per Capita Confirmed Cases and Deaths by Country
Latest per Capita Counts
Latest Top 12 Countries Total Confirmed Cases and Deaths
Latest Top 12 Total Cases and Deaths
Selected Countries Timeline
Confirmed Cases: Country Comparison
Deaths: Country Comparison
Selected U.S. States Timeline
Confirmed Cases: U.S. States Comparison
Deaths: U.S States Comparison
Mortality
Mortality
3-day Change by Country
3-day Change in Confirmed Cases
3-day Change in Deaths
Doubling Rates Countries
Doubling Rate in Days: Confirmed Cases
Doubling Rate in Days: Deaths
Doubling Rates U.S.
Doubling Rate in Days: Confirmed Cases (U.S.)
Doubling Rate in Days: Deaths (U.S)

Suggested Reading
Data Sources

Introduction: Population Normalization and Logarithmic Scaling

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These plots show the daily status of COVID-19 cases as reported by Johns Hopkins University. We want to caveat the data analysis by two main points:

  • Keep in mind that data are changing frequently as more Covid-19 testing becomes more readily available globally.
  • Use your own judgement on the trustworthiness on reported data, i.e. where the data come from. (see section on Data Sources)

We chose to plot totals and numbers normalized by population (expressed as per 100,000). Also, it is advantageous to plot case totals (confirmed infections, deaths, and recovered) in logarithmic scale where trends and parallels between countries become more obvious. Note, that a straight line trending upwards in logarithmic scale indicates exponential increase! Taking a close look at the plots, one will discern differences and similarities, and that for the most part initial stages are similar in all countries with a time lag. What to look out for is whether the measures taken by countries, foremost social distancing show the desired effects of slowing and eventually levelling out the exponential upwards trends. We produce plots for confirmed cases and deaths, which may be somewhat more reliable with respect to an impact for a country while tests are rolled out in larger numbers. We also plot mortality rates, 3-day change curves and doubling rates of confirmend cases and deaths.

This is a work in progress, stay tuned.

You can get the notebook underlying this work at: https://github.com/EarthBigData/covid19

Interacting with the plots

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Hover: See actual numbers when hovering over a plot

Control buttons: Interact with the plots, e.g. Zoom in/out, Reset, Save Plot

Labels: In the legend, click on label to dim/highlight a specific country or state.

Confirmed Cases: Status for United States

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Confirmed Cases: Status for Germany

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Latest Confirmed Cases and Deaths Per Capita: Country Comparison

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Latest Top 12 Countries Total Confirmed Cases and Deaths

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Confirmed Cases: Country Comparison

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Deaths Country Comparison

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Confirmed Cases: U.S. States Comparison

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U.S.Deaths

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Mortality Rates Country Comparison

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The plot below shows the mortality rate in percent computed as:

$Mortality=\frac{Deaths}{Infected} * 100$

Two caveats:

  • Because there is a timelag from infection to death, the rates computed on a daily basis might be underestimating the rate.
  • The rates might be vastly overestimated because the lag in testing is widespread. South Korea has done the most intensive testing, hence South Korea data seem most reliable for mortality assessment.

3-Day Change in Confirmed Cases: Country Comparison

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The plots below show the change of total number of confirmed cases compared to three days before the plotted date. A factor 2 means the cases doubled after three days. A factor 1 means no new confirmed cases are reported compared to three days before. (Plots also inspired by Jennifer Bardwell, Jim Bardwell).

3-Day Change in Deaths: Country Comparison

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The plots below show the change of total number of deaths compared to three days before the plotted date. A factor 2 means the cases doubled after three days. A factor 1 means no new deaths are reported compared to three days before. (Plots also inspired by Jennifer Bardwell, Jim Bardwell).

Number of Days to Double Confirmed Cases: Country Comparison

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The plots below show the change rate (${doubling.rate}_{confirmed.cases}$) in number of days for confirmed cases to double ($days_{confirmed.cases.double}$). This is expressed as

${doubling.rate}_{confirmed.cases} = \frac{1}{days_{confirmed.cases.double}}$

In this representation a factor of 1 means cases double every day, 0.5 means cases double every 2nd day, 0.33 means cases double every third daty, 0.25 menas cases double every 4th day, etc. When the line approaches 0, no more cases are identified.
Plots begin at more than 100 confirmed cases.
(Plots inspired by Jennifer Bardwell, Jim Bardwell).

Number of Days to Double Deaths: Country Comparison

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The plots below show the change rate (${doubling.rate}_{deaths}$) in number of days for deaths to double ($days_{deaths.double}$). This is expressed as

${doubling.rate}_{deaths} = \frac{1}{days_{deaths.double}}$

In this representation a factor of 1 means death counts double every day, 0.5 means death counts double every 2nd day, 0.33 means death counts double every third day, 0.25 menas death counts double every 4th day, etc. When the line approaches 0, no more deaths are counted.
Plots begin at more than 25 deaths.
(Plots inspired by Jennifer Bardwell, Jim Bardwell).

Number of Days to Double Confirmed Cases: U.S. States Comparison

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The plots below show the change rate (${doubling.rate}_{confirmed.cases}$) in number of days for confirmed cases to double ($days_{confirmed.cases.double}$). This is expressed as

${doubling.rate}_{confirmed.cases} = \frac{1}{days_{confirmed.cases.double}}$

In this representation a factor of 1 means cases double every day, 0.5 means cases double every 2nd day, 0.33 means cases double every third daty, 0.25 menas cases double every 4th day, etc. When the line approaches 0, no more cases are identified.
Plots begin at more than 100 confirmed cases.
(Plots inspired by Jennifer Bardwell, Jim Bardwell).

Number of Days to Double Deaths: U.S. States Comparison

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The plots below show the change rate (${doubling.rate}_{deaths}$) in number of days for deaths to double ($days_{deaths.double}$). This is expressed as

${doubling.rate}_{deaths} = \frac{1}{days_{deaths.double}}$

In this representation a factor of 1 means death counts double every day, 0.5 means death counts double every 2nd day, 0.33 means death counts double every third day, 0.25 menas death counts double every 4th day, etc. When the line approaches 0, no more deaths are counted.
Plots begin at more than 10 deaths.
(Plots inspired by Jennifer Bardwell, Jim Bardwell).

We hope these data are informative and convey how seriously we have to take the COVID-19 pandemic. Stay safe.

Suggested Reading:

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Coronavirus Interview with Larry Brilliant

Data Sources

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Johns Hopkins University daily updated COVID-19 data

COVID-19 confirmed cases, deaths and recovered cases data are streamed from the The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The CCSE COVID-19 GitHub Repo has more information about these data and their sources.

Since Johns Hopkins changed the data format on 2020-03-23, they do not provide the US compiled time series data yet. We acknowldege Sooth Sawyer who helped out by compiling the data set in the old format at: https://www.soothsawyer.com

UN Population Data

We obtain the Population data from UN statistics. UN Population Data Sets have more information about these data and their sources.

US Population Data

US population data ar obtained from US Census statistics. US Population Data Sets have more information about these data and their sources.